Long COT logic improves the influence of larger language models on complex tasks but comes with drawbacks. The typical “think-back-and-answer” method slows the response time, disrupting real-time interactions like chatbots. It also risks inaccuracies, as errors in previous logic steps can lead to a misleading final answer. Unlike humans, which often shares partial thoughts or conclusions during the conversation, delays LLMS reactions until all logic is met. While RL is commonly used to train logic models, it mainly rewards the final answers, ignoring useful intermediate insights. Interest in models of alternative education between consideration and answers, but this is a challenge.
RL has become a popular method for increasing logic in LLMS, considering its success in organizing models with human preferences. Two common reward types RL: Result-based rewards (ORM), which focuses on the final answer, and process-based rewards (PRM), which respond to intermediate logic measures. When the PRM is monitored in more detail, they often depend on human OT notation and additional models, making them susceptible to complex and reward hacking. Separately, efforts to improve the LLM logic have been invented to reduce strategy, structural logic, tool integration and delay and improve efficiency.
Apple Pal and Duke University researchers presents interlaved logic, a new RL approach that enables language models to alternate between ideas and answers when solving complex, multi-step questions. Instead of waiting until the end to answer, models provide informative intermediate answers, which improve feedback for users and guide their logic. Using a direct rule -based prize, the model is trained to produce helpful logic steps, which leads to faster response and 19.3% better accuracy. Trained only on QA and logic datasets, the method shows a strong generalization in the more challenging benchmarks such as mathematics, GPQA and MMLU.
This study proposes a reinforcement learning framework to train LLMS for interlaved logic, where models between internal thinking and user-facing intermediate answers are optional. Once the model reaches a meaningful target in logic, each intermediate step or “sub-aid” is distributed. A special training sample with
The interlaved logic approach was evaluated on both familiar and unknown datasets using QWEN2.5 models (1.5b and 7B). Unlike traditional methods that separate the idea and answer, the interlaved method provides answers by improving both motion and utility. When combined with intermediate rewards, it significantly increases the model performance when the response reduces the delay by more than 80%. Even without contact with new domains during training, the model shows strong generalization. These results highlight the value of interlaved logic to AI systems more responsive and real-world, to respond to more and effective in multi-step logic tasks.
In conclusion, the study finds how the interlaved logic – where alternative models models can significantly improve influence and response between logic and intermediate answers. Using the QWEN2.5-1.5B model, the authors show that providing timely intermediate response during training increases accuracy and accelerates the response generation. Various RL strategies were tested, including the PPO. Showing stable results, and conditional, time-discount rewards prove to be the most effective. This method gives the scales well to the complex functions and pushes the traditional think-to-water basins. Unlike token-level reward models, this approach employs simple rule-based rewards after completing complete logic measures, avoiding hacking prizes. Ultimately, the interlaved logic increases the quality and efficiency of the logic without depending on the external equipment.
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Sana Hassan, a consulting intern at MarktecPost and IIT Madras, is enthusiastic about applying technology and AI to overcome real-world challenges. With more interest in solving practical problems, it brings a new perspective to the intersection of AI and real life solutions.
